Introduction to Deep Learning in Python

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Course Description

Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks and generate predictions with them.

In this chapter, you'll use the Keras library to build deep learning models for both regression and classification. You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions, and by the end of the chapter, you'll have all the tools necessary to build deep neural networks.

Learn how to optimize the predictions generated by your neural networks. You'll use a method called backward propagation, which is one of the most important techniques in deep learning. Understanding how it works will give you a strong foundation to build on in the second half of the course.

Learn how to optimize your deep learning models in Keras. Start by learning how to validate your models, then understand the concept of model capacity, and finally, experiment with wider and deeper networks.

1
Basics of deep learning and neural networks
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In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks and generate predictions with them.
View Chapter Details Play Chapter Now
Optimizing a neural network with backward propagation

Learn how to optimize the predictions generated by your neural networks. You'll use a method called backward propagation, which is one of the most important techniques in deep learning. Understanding how it works will give you a strong foundation to build on in the second half of the course.
View Chapter Details Play Chapter Now
Building deep learning models with keras

In this chapter, you'll use the Keras library to build deep learning models for both regression and classification. You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions, and by the end of the chapter, you'll have all the tools necessary to build deep neural networks.
View Chapter Details Play Chapter Now
Fine-tuning keras models

Learn how to optimize your deep learning models in Keras. Start by learning how to validate your models, then understand the concept of model capacity, and finally, experiment with wider and deeper networks.
View Chapter Details Play Chapter Now

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Dan Becker

Data Scientist and contributor to Keras and TensorFlow libraries

Dan Becker is a data scientist with years of deep learning experience. He has contributed to the Keras and TensorFlow libraries, finishing 2nd (out of 1353 teams) in the $3million Heritage Health Prize competition, and supervised consulting projects for 6 companies in the Fortunate 100. He previously worked as a data scientist at Google. Now he is the CEO and co-founder of Decision.ai, which helps companies apply their machine learning models to make better real-world decisions.

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Introduction to Deep Learning in Python

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Loved by learners at thousands of top companies:

Course Description

Basics of deep learning and neural networks

Introduction to deep learning

Comparing neural network models to classical regression models

Forward propagation

Coding the forward propagation algorithm

Activation functions

The Rectified Linear Activation Function

Applying the network to many observations/rows of data

Deeper networks

Forward propagation in a deeper network

Multi-layer neural networks

Representations are learned

Levels of representation

Building deep learning models with keras

Creating a keras model

Understanding your data

Specifying a model

Compiling and fitting a model

Compiling the model

Fitting the model

Classification models

Understanding your classification data

Last steps in classification models

Using models

Making predictions

Optimizing a neural network with backward propagation

The need for optimization

Calculating model errors

Understanding how weights change model accuracy

Coding how weight changes affect accuracy

Scaling up to multiple data points

Gradient descent

Calculating slopes

Improving model weights

Making multiple updates to weights

Backpropagation

The relationship between forward and backward propagation

Thinking about backward propagation

Backpropagation in practice

A round of backpropagation

Fine-tuning keras models

Understanding model optimization

Diagnosing optimization problems

Changing optimization parameters

Model validation

Evaluating model accuracy on validation dataset

Early stopping: Optimizing the optimization

Experimenting with wider networks

Adding layers to a network

Thinking about model capacity

Experimenting with model structures

Stepping up to images

Building your own digit recognition model

Final thoughts